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William Gilpin; Cellular Automata As Convolutional Neural Networks.pdf

1809.02942.pdf
Preview of William Gilpin; Cellular automata as convolutional neural networks
🔗 Source: arxiv.org
📊 Size: 4.51 MB
📄 Pages: 12 pages
⬇️ Downloads: 127

Summary

Deep learning techniques have demonstrated success in predicting complex dynamical systems, motivating questions about how neural networks encode and represent dynamical rules. Researchers explored this problem in the context of cellular automata (CA), simple dynamical systems that are intrinsically discrete. They showed that any CA can be represented using a convolutional neural network with a network-in-network architecture and developed a general convolutional multilayer perceptron architecture that can learn the dynamical rules for arbitrary CA when given videos of the CA as training data. The study found that CA with simpler rule tables produce trained networks with hierarchical structure and layer specialization, while more complex CA produce shallower representations, illustrating how the underlying complexity of the CA's rules influences the specificity of these internal representations.

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Deep learning techniques have demonstrated success in predicting complex dynamical systems, motivating questions about how neural networks encode and represent...

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  • File Format: PDF
  • File Size: 4.51 MB
  • Pages: 12
  • Language: EN
  • Total Downloads: 127
  • Last Updated: 6 days ago

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